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All Journal Explore: Jurnal Sistem Informasi dan Telematika (Telekomunikasi, Multimedia dan Informatika) EXPERT: Jurnal Manajemen Sistem Informasi dan Teknologi Jurnal Pilar Nusa Mandiri International Journal of Research in Community Services International Journal of Quantitative Research and Modeling Jurnal Pengabdian Masyarakat Tapis Berseri SisInfo : Jurnal Sistem Informasi dan Informatika INTERNAL (Information System Journal) Bulletin of Intelligent Machines and Algorithms
Nursyanti, Reni
Fakultas Teknologi Dan Informatika, Universitas Informatika Dan Bisnis Indonesia
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Engineering Environmental Science Physics Social Sciences Other

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Data Mining Implementation Using Naïve Bayes Algorithm and Decision Tree J48 In Determining Concentration Selection Budiman Budiman; Reni Nursyanti; R Yadi Rakhman Alamsyah; Imannudin Akbar
International Journal of Quantitative Research and Modeling Vol. 1 No. 3 (2020): International Journal of Quantitative Research and Modeling
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijqrm.v1i3.72

Abstract

Computerization of society has substantially improved the ability to generate and collect data from a variety of sources. A large amount of data has flooded almost every aspect of people's lives. AMIK HASS Bandung has an Informatic Management Study Program consisting of three areas of concentration that can be selected by students in the fourth semester including Computerized Accounting, Computer Administration, and Multimedia. The determination of concentration selection should be precise based on past data, so the academic section must have a pattern or rule to predict concentration selection. In this work, the data mining techniques were using Naive Bayes and Decision Tree J48 using WEKA tools. The data set used in this study was 111 with a split test percentage mode of 75% used as training data as the model formation and 25% as test data to be tested against both models that had been established. The highest accuracy result obtained on Naive Bayes which is obtaining a 71.4% score consisting of 20 instances that were properly clarified from 28 training data. While Decision Tree J48 has a lower accuracy of 64.3% consisting of 18 instances that are properly clarified from 28 training data. In Decision Tree J48 there are 4 patterns or rules formed to determine concentration selection so that the academic section can assist students in determining concentration selection.
Kolaborasi Kreatif Manusia dan Ai Untuk Generasi Masa Depan R. Yadi Rakhman Alamsyah; Reni Nursyanti; Anggi Dewi Nurcahyani
Jurnal Pengabdian Masyarakat Tapis Berseri (JPMTB) Vol. 5 No. 1 (2026): Jurnal Pengabdian Masyarakat Tapis Berseri (JPMTB) (Edition April)
Publisher : Pusat Studi Teknologi Informasi Fakultas Ilmu Komputer Universitas Bandar Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36448/jpmtb.v5i1.181

Abstract

Digital transformation is currently dominated by developments in Artificial Intelligence (AI), which are bringing fundamental changes to the creative industry. For today’s youth, particularly vocational high school (SMK) students, mastering AI is no longer just an option but a necessity to maintain relevance and competence in the future.This Community Service Program (PkM) aims to equip students of SMK Bakti Nusantara 666 with a deep understanding of the creative collaboration between humans and AI. Through methods including counseling, practical mentoring in prompt engineering techniques, and evaluations via pre-tests and post-tests, this program targets an 80% increase in participants' digital literacy.The primary focus of this activity is to position AI as an 'intelligent assistant' that expands the imagination without eliminating the originality of human ideas, while consistently upholding ethics and copyright. The results of this program are expected to produce digital talents who are innovative, responsible, and ready to contribute to the creative economy.
Enhancing Breast Cancer Diagnosis with Ensemble Learning: Leveraging Convolutional Neural Networks and Pretrained Models through Averaged Predictions Elia Setiana; Reni Nursyanti; Nur Alamsyah; Nayla Nurul Azkiya
Bulletin of Intelligent Machines and Algorithms Vol. 1 No. 4 (2026): BIMA May 2026 Issue
Publisher : Maheswari Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65780/bima.v1i4.26

Abstract

Breast cancer continues to be a serious health issue at the global level, and early detection can significantly improve patient outcomes. This research uses imaging techniques to examine the design of an improved classification model in breast cancer detection. This project uses deep learning approaches through Convolutional Neural Networks (CNN) and ensemble learning models to potentially improve classification accuracy. To further enhance performance while controlling for class imbalance and overfitting, we leverage several models, such as ResNet18 and VGG16, with data augmentation and pre-trained models. Our methods included standard preprocessing of medical images, splitting datasets into training, testing and validation sets, and training each model with the Adam optimizer. Performance measurement included accuracy, precision, recall, and F1 score metrics. Overall, prototypes recently created displayed clear advantages based on finding results achieved through an ensemble method, which demonstrated improved model stability and reduced significant misclassification errors, and model accuracy reached 0.96. This research is crucial while developing strong deep-learning models to aid in breast cancer detection, ultimately allowing us to set a base for developing better diagnostic inference systems in medical-based applications. These systems may help improve early detection and overall patient care.
ISOLATION FOREST PARAMETER TUNING FOR MOBILE APP ANOMALY DETECTION BASED ON PERMISSION REQUESTS Valencia Claudia Jennifer Kaunang; Nur Alamsyah; Reni Nursyanti; Budiman Budiman; Venia R Danestiara; Elia Setiana
Jurnal Pilar Nusa Mandiri Vol. 21 No. 2 (2025): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Pe
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/pilar.v21i2.6647

Abstract

Ensuring mobile app security needs the capability to detect apps that request excessive or inappropriate permissions. This research proposes an anomaly detection approach using Isolation Forest, enhanced through hyperparameter tuning, to identify suspect apps based on permission request patterns. The dataset is processed into binary features, followed by exploratory data analysis (EDA) to examine the distribution and highlight sensitive permissions. The Isolation Forest model is then optimized by tuning parameters such as contamination level, number of estimators, and sample size. The fine-tuned model achieved a more accurate separation between normal and anomaly applications, detecting 10 anomalies out of 200 applications, with anomaly applications averaging 125.10 permits compared to 42.76 in normal applications. These anomalies often requested permissions related to network, storage, contacts and microphone, indicating potential privacy risks. The results show that parameter tuning improves the detection performance of Isolation Forest, providing a practical solution for mobile security monitoring. After tuning, the number of false positives decreased by 50%, and the model successfully reduced detected anomalies from 20 to 10, increasing the precision of anomaly detection from 70% to 90%. Future work could include improving feature selection and integration into real-time detection systems. 
Rancang Bangun Media Informasi Berbasis Multimedia Untuk Mencegah Risiko Stunting Pada Anak Balita R. Yadi Rakhman Alamsyah; Reni Nursyanti; Resvina Alya Putri
INTERNAL (Information System Journal) Vol. 6 No. 2 (2023)
Publisher : Masoem University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32627/internal.v6i2.869

Abstract

Stunting is a growth disorder that describes the failure to growth potential as a result of inadequate health status or nutrition. Child stunting data in 2022 has fallen to 21.6%, but according to the World Health Organization (WHO) criteria, the percentage is still high (20%). One prevention in reducing the risk of stunting is to increase the knowledge of parents about stunting and the intake of good food to be consumed by children. The dissemination of information about stunting and nutritional food intake is often found in the media of information such as text, video, and images but the distribution of information is not organized in one medium. The development phase uses the Multimedia Development Life Cycle (MDLC) methodology with five (five) stages: concept, design, material collection, assembly, and testing. The result of this study is a multimedia-based information media in dasawisma RT 05 Bumi Orange, Cimekar Village, Bandung Regency, with a selection of stunting material menus including complementary foods for breast milk (MPASI), children's weight and height standards, child nutrition, and food intake.
Co-Authors Akbar, Imannudin Aldi Rohman Anggi Dewi Nurcahyani Asep Ramdani Budiman Cecep Kurniawan Chairul Habibi Gagas Langgeng Dwi Prastyo Graha Prakarsa Luthfi Fadle Rynaldy Muhammad Syarif Hartawan Nasution, Vani Maharani Nayla Nurul Azkiya Nur Alamsyah Nur Alamsyah R. Yadi Rakhman R. Yadi Rakhman Alamsyah R.Yadi Rakhman Alamsyah Resvina Alya Putri Sardjono Sardjono Setiana, Elia Surya Perdana Valencia Claudia Jennifer Kaunang Venia R Danestiara